System coordinator and modular architecture for open-loop and closed-loop control of diabetes

ABSTRACT

A structure, method, and computer program product for a diabetes control system provides, but is not limited thereto, the following: open-loop or closed-loop control of diabetes that adapts to individual physiologic characteristics and to the behavioral profile of each person. An exemplary aspect to this adaptation is biosystem (patient or subject) observation and modular control. Consequently, established is the fundamental architecture and the principal components for a modular system, which may include algorithmic observers of patients&#39; behavior and metabolic state, as well as interacting control modules responsible for basal rate, insulin boluses, and hypoglycemia prevention.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority from U.S. application Ser. No.13/322,943, filed Nov. 29, 2011, entitled “System Coordinator andModular Architecture for Open-Loop and Closed-Loop Control of Diabetes;”of which the disclosure is hereby incorporated by reference herein inits entirety.

FIELD OF THE INVENTION

Some aspects of some embodiments of this invention are in the field ofmedical methods, systems, and computer program products related tomanaging the treatment of diabetic subjects, more particularly toglycemic analysis and control.

BACKGROUND OF THE INVENTION

People with diabetes face a life-long optimization problem: to maintainstrict glycemic control without increasing their risk for hypoglycemia[13,58,59]. The engineering challenge related to this problem is todesign algorithms using automated insulin delivery to exert optimalclosed-loop control of glucose fluctuations. Since the early studies ofcontinuous external glucose regulation (e.g., BioStator, [10]), twoprimary approaches have emerged: The use of classicproportional-Integral-derivative (PID) algorithms, and modem methodsbased on models of the human metabolism. The first studies usingsubcutaneous insulin delivery and continuous glucose monitoring (CGM)employed PID control [57,60]. Recently, model predictive control (MPC),received considerable attention [20,21,44,50] due to its many clinicaland engineering advantages.

BRIEF SUMMARY OF THE INVENTION

MPC is typically based on a model of the human metabolic system.Fortunately, the modeling of glucose-insulin interaction is one of themost advanced applications of mathematics to medicine. Beginning withthe now classic Minimal Model of Glucose Kinetics (MMGK) co-authored byDr. Claudio Cobelli who leads the Italian team of this project [2], anumber of elaborate models have been developed [16,21]. These models canbe classified in three broad classes: (i) models to measure parametersthat are not accessible by direct lab tests, such as MMGK assessinginsulin sensitivity; (ii) models to simulate that enable in silicopre-clinical trials, and (iii) models to control used to empoweralgorithms such as MPC.

An aspect of an embodiment of the present invention provides theprogress towards advisory open-loop control or automated closed-loopcontrol that will be greatly accelerated by a structured modularapproach to building control components. Specifically, an aspect of anembodiment of the present invention provides a system of control modulesresponsible for basal rate, pre-meal and correction insulin boluses, andhypoglycemia prevention. These modules will be informed by biosystemobservers providing information about the patients' glycemic state. Amodular approach to closed-loop control development would have a numberof advantages that include, but are not limited to:

-   -   Incremental testing of modules in parallel or consecutive        studies;    -   Incremental FDA approval and industrial deployment of system        features;    -   User flexibility—each system observer or control module could be        used separately, or within an integrated control system,        depending on patients' or physicians' choice;

An aspect of an embodiment provides an external open-loop or closed-loopcontrol that shall have separate interacting components responsible forprevention of hypoglycemia, postprandial insulin correction boluses,basal rate control, and administration of pre-meal boluses. Thesecontrol modules receive information from biosystem observers that areresponsible for tracking glucose fluctuations and the amount of activeinsulin at any point in time. This dual control-observer architecture isdictated by the natural separation of the computational elements of aclosed-loop control system into algorithms observing the person andalgorithms actuating control. Central role in this architecture isplayed by the system coordinator—an algorithmic module that isresponsible for controlling the integration and the interactions of themodular system.

Pertaining to the feasibility of each of the proposed observers andcontrol modules, as well as the feasibility of using CGM technology andsubcutaneous insulin delivery for automated closed-loop control thefollowing may be referenced:

-   -   Use and accuracy of CGM; algorithmic processing of CGM: data:        [3,5,24,26,29,34,40];    -   In silica pre-clinical trials: [15,16,28,31];    -   Glucose variability observer and Risk Analysis:        [36,37,38,39,42,43,46];    -   Insulin observer, subcutaneous insulin transport, sensitivity,        and action: [0,2,7,12,47,54];    -   Control module 1: prediction and prevention of hypoglycemia:        [4,26,35,53,56,61];    -   Control modules 2, 3, and 4: correction boluses and closed-loop        control: [8,11,27,44,45,51].

An embodiment of the present invention defines a modular architecturethat can accommodate a variety of system observers and control modulesthat can be assembled into a system for open-loop advisory mode controlor automated closed-loop control of diabetes.

An aspect of an embodiment of the present invention, a structure,method, and computer program product for a diabetes control systemprovides, but is not limited thereto, the following: open-loop orclosed-loop control of diabetes that adapts to individual physiologiccharacteristics and to the behavioral profile of each person. Anexemplary aspect to this adaptation is biosystem (patient) observationand modular control. Consequently, an aspect of an embodiment of thepresent invention establishes the fundamental architecture and theprincipal components for a modular system, which includes algorithmicobservers of patients' behavior and metabolic state, as well asinteracting control modules responsible for basal rate, insulin boluses,and hypoglycemia prevention. An exemplary role in this architecture isplayed by the system coordinator—such as an algorithmic module that maybe responsible for controlling the integration and the interactions ofthe modular system.

An aspect of an embodiment of the present invention provides astructure, method, and computer program product for a diabetes controlsystem provides, but is not limited thereto, the following: open-loop orclosed-loop control of diabetes that adapts to individual physiologiccharacteristics and to the behavioral profile of each person. Anexemplary aspect to this adaptation is biosystem (patient) observationand modular control. Consequently, an aspect of an embodiment of thepresent invention establishes the fundamental architecture and theprincipal components for a modular system, which may include algorithmicobservers of patients' behavior and metabolic state, as well asinteracting control modules responsible for basal rate, insulin boluses,and hypoglycemia prevention.

An aspect of an embodiment of the present invention provides a structurefor a diabetes control system. The structure may comprise: modules forprocessing and storing data; conduits between modules; and signalsproduced in the event that certain modules are not inserted within thestructure.

An aspect of an embodiment of the present invention provides a computerprogram product comprising a computer useable medium having a computerprogram logic for enabling at least one processor in a computer systemfor a diabetes control system. The computer program logic may beconfigured to include: modules for processing and storing data; conduitmeans between modules; and producing signals in the event that certainmodules are not inserted within the system.

An aspect of an embodiment of the present invention provides a methodfor enabling a diabetes control system. The method may comprise:providing modules for processing and storing data; providing conduits orthe like between modules; and producing signals in the event thatcertain modules are not inserted within the system.

These and other objects, along with advantages and features of variousaspects of embodiments of the invention disclosed herein, will be mademore apparent from the description, drawings and claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated into and form a partof the instant specification, illustrate several aspects and embodimentsof the present invention and, together with the description herein,serve to explain the principles of the invention. The drawings areprovided only for the purpose of illustrating select embodiments of theinvention and are not to be construed as limiting the invention.

FIG. 1 schematically provides an exemplary embodiment of the structureof the modular architecture for control of diabetes.

FIG. 2 schematically provides an exemplary decoupled basal and boluscontrol supervised by hypoglycemia prevention.

FIG. 3 schematically provides a first exemplary embodiment of thegeneral architecture for control of diabetes.

FIG. 4 schematically provides a detailed embodiment of the generalarchitecture for control of diabetes.

FIG. 5 schematically provides an exemplary embodiment of the generalarchitecture for control of diabetes.

DETAILED DESCRIPTION OF TILE INVENTION Definitions

For the purposes of an embodiment of the present invention:

-   -   Open-loop advisory-mode control is defined as a system that uses        CGM data and information from insulin pump to provide real-time        advice about treatment adjustments to patients with diabetes;    -   Closed-loop control system is defined as a system using data        from CGM devices and information from insulin pump to        automatically control insulin delivery by the insulin pump.

Overview of Modular Architecture of Open-Loop and Closed-Loop Control ofDiabetes:

As shown in FIG. 1 , a modular open- or closed-loop control systemincludes observers of patients' metabolic state, daily profiles andbehavior, which provide information to the central system coordinator,which in turn directs the actions and the interactions of an array ofcontrol modules. Specifically:

A System Coordinator will coordinate the distribution of input signalsto control modules (routing) and most importantly allocate differentsegments of diabetes management to different controllers by restrictingthe input to these controllers (see example in discussion below).Finally, the system coordinator will ensure the direct feeding ofexternal inputs to controllers if the observers are inactive.

Observers will receive frequent information about metabolic measurements(such as continuous glucose or insulin), metabolic disturbances (such asmeals or exercise), and metabolic treatments (such as insulin orglucagon injections). Based on these inputs the observers will

-   -   construct and update an internal representation of the metabolic        state of the patient and transmit this state to the control        modules;    -   keep an internal representation of the behavioral pattern of the        patient, such as daily meal and exercise profiles; and    -   assess risks for undesirable events such as hypoglycemia or        glucose variability.

Safety Supervision. Module will receive information from the observersand from the control modules (below) and will decide whether there is anincrease of the risk for upcoming hypoglycemia or prolongedhyperglycemia. If risk increase in encountered, the module will reduceor discontinue the suggested insulin infusion.

Three Control Modules will be responsible for insulin administration.The Control Modules will receive instructions from the SystemCoordinator and will supply their output to the Safety SupervisionModule for evaluation. The Control Modules are:

-   -   Control Module 1 will calculate and suggest the basal insulin        delivery;    -   Control Module 2 will calculate and suggest compensation of the        basal delivery (up or down) in case of non meal related        deviations, such as drops or rise due to exercise, or residual        dawn phenomenon not covered by module 1;    -   Control Module 3 will calculate and suggest meal insulin        boluses, potentially including pre-meal priming boluses.

While the Safety Supervision Module and the Control Modules are subjectsof independent invention disclosures or have been developed elsewhere[44,51], the subject of an embodiment the present invention is definingthe general architecture of an open-loop or a closed-loop control systemand the interactions between the components (modules) of that system aspresented in FIGS. 1 and 2 . A feature of an embodiment of the presentinvention is the design of the System Coordinator, responsible for theseamless integration of observers, control modules, and safetysupervision.

In the context of engineering design of an embodiment, it may beimportant to underscore that:

-   -   Each system observer or control module can be used separately,        or within an integrated open- or closed-loop control system,        depending on patients' or physicians' choice. This modular        approach will allow the incremental testing of system features        in parallel or consecutive studies;    -   The operation of the system in open-loop advisory mode will be        conceptually similar to closed-loop control mode, but will        differ in terms of implementation: open-loop will provide        real-time information to the patient, while closed-loop will        control directly the insulin pump.

An exemplary idea behind the introduction of a System Coordinator is itsability to decouple different control functions, and to coordinate thecontrol module action with separate modules responsible for differentaspect of diabetes management, such as meals, exercise, basal pattern,and hypo/hyperglycemia avoidance. In other words, separate interactingalgorithms will suggest optimal pre-meal bolus control (e.g. astochastic algorithm) and will exercise basal rate control or administerpost-meal correction boluses (e.g. deterministic algorithms). This“separation of duties” corresponds to the stochastic nature of meals andbehavior, and the deterministic nature of basal and postprandialphysiology, and also has deep mathematical reasoning motivated by theexperience gained in our recent clinical trials of closed-loop controlin Type 1 diabetes. These trials showed excellent overnight regulationbut rather slow (as compared to open loop) breakfast regulation. Tuningof the algorithm aggressiveness alone was not sufficient to achieve bothgoals. It was therefore necessary to introduce a strategy that handlesdifferently night and breakfast regulation. With this in mind, weintroduce the System Coordinator, which allows each Control Module tooperate within a certain BG range. Specifically, after Module 3 treats ameal, the effect of this bolus and the concurring meal is projected 1-2h ahead and will be subtracting in real time from the trajectory fromthe incoming CGM data. In other words, the System Coordinator will“correct” the CGM track sent to Module 2 by the projected action ofModule 3. This modus operandi is illustrated in FIG. 2 : the observedglucose trace is presented by a gray line, but Module 2 will only “see”the black trace, which is the difference between real-time CGM and theaction of Module 3. This will result in a rather simple interactionbetween Modules 1, 2 and 3: Module 1 sets the reference treatment forthe day, Module 3 will operate in a regime with updates every severalhours, its action will be continuously projected, and the result will besupplied to Module 2, which will operate in frequent increments (e.g. 15minutes)

Further, all control modules will be supervised by the SafetySupervision module, which will warn the person for upcominghypo/hyperglycemia and will suggest reduction in insulin delivery orcorrection boluses in open-loop advisory mode, or will directly reduceor discontinue the insulin pump infusion rate in closed-loop controlmode.

FIG. 3 presents a flow chart of the interactions between the systemcomponents identified in FIG. 1 , while FIG. 4 presents a detailed viewof the nodes and the conduits of the modular architecture, featuring theinteractions of the System Coordinator with the other components of anopen-loop advisory system or a closed-loop control system. The onlydifference between open-loop advisory operation and closed-loop controloperation is in the delivery of information from the System Coordinator:in open-loop the information about insulin rate and potential forhypoglycemia is presented to the patient; in closed-loop control modeinsulin delivery commands are send directly to the insulin pump. Thefollowing paragraphs describe the function of each of the modules of thearchitecture, focusing largely on the interfaces between modules and therequirements/definition of each signal. This description assumes thatthe system updates are computed in discrete time t, corresponding togiven sampling interval. We use the terms “time,” “discrete time,” and“stage” interchangeably.

The Data Module serves to process raw data from external sources, scanthe data for integrity, and produce four real-time signals that are thenused by the remaining modules of the modular system. As shown in FIG. 4, the external data sources are (1) continuous glucose monitoring (CGM)data, possibly from multiple sensors, (2) data from the patient'sinsulin pump (i.e. insulin actually delivered to the patient), (3)exercise information (e.g. heart rate information and other indicatorsof physical activity), and (4) meal information (e.g. acknowledgementsof meals as they arrive). Only the CGM and the insulin pump data aremandatory; exercise and meal information is optional and will be usedonly in certain embodiments of this invention. The outputs of the datamodule are:

-   -   g_(c)(t)=a single, processed glucose sample at stage t,        reflecting the effect of all processing of the glucose sensors        used as input    -   J(t) the most recent (relative to t) actual insulin pump command        (mU/min) It should be noted that all insulin injections are        expressed as rates of infusion, including boluses.    -   EX(t)=exercise process data at time t (EX is an optional time        series that is specific to the embodiment of the modular        architecture)    -   M(t) meal data at stage t (M is an optional time series that is        specific to the embodiment of the modular architecture)

The Long Term Observer Module serves to produce statistical profiles ofthe patient's meal and exercise behavior, β and η respectively, and acircadian profile the patient's daily insulin utilization γ, which areused by other control modules to set priors on parameter estimatesand/or constraints on insulin action. The inputs to the Long TermObserver Module are all of the Data Module outputs shown in FIG. 4 :g_(c)(t), J(t), EX(t), and M(t). The outputs of the module are:

-   -   β(τ)=a daily profile of the patient's meal behavior, where the        notation (τ) expresses the fact that the profile is used as a        lookup table descriptive of the patients behavior as a function        of time of day    -   η(τ)=a daily profile of the patient's exercise behavior, where        again the notation (τ) expresses the fact that the profile is        used as a lookup table descriptive of the patients behavior as a        function of time of day    -   γ(τ)=a daily (circadian) profile of the patient's utilization of        insulin, where again the notation (τ) expresses the fact that        the profile is used as a lookup table descriptive of the        patients behavior as a function of time of day        All outputs of the Long Term Observer Module are optional—the        entire Long-Term Observer would only exist in certain specific        embodiments of the modular architecture. Some embodiments of the        modular architecture will not include an active Long Term        Observer; in these embodiments the outputs of the model take        default values, β⁰(τ) η⁰(τ), and γ⁰(τ), which are not utilized        by the remaining modules of the system.

In one embodiment of the Long Term Observer Module, the meal behavioralprofile β defines a probabilistic description of the patients eatingbehavior within given “meal regimes” throughout the day. Specifically,for each meal regime (3 would comprise the conditional probability of ameal arriving within the next sampling interval (within the meal'swindow of opportunity), given that the meal has not yet arrived:

$\begin{matrix}{p_{k} = \frac{f_{k}}{1 - F + {\sum_{i = 1}^{\overset{\_}{k}}f_{i}}}} & (1)\end{matrix}$

where f_(k) is the frequency with which the corresponding meal arrivesin the k-th sampling interval of the current meal regime, 1-F is theprobability with which the meal will not arrive within its window ofopportunity, and k is the latest possible stage in which the meal couldarrive. In this scheme it is historical information about meals M(t)that allows for the “observation” of the meal behavioral profile β(τ),expressed as conditional probabilities p_(k). Note that if the meal timeis known in advance within the j-th sampling interval, then pi, will bezero for all k not equal to j, and p_(j)=1. This embodiment of the mealbehavioral profile would allow for the administration of insulin inanticipation of meals, without compromising patient safety.

The Short Term Observer Module of FIGS. 3 and 4 serves to computereal-time estimates of key metabolic states for the patient, includingpossibly plasma insulin and glucose concentration, which are used byControl Modules 1-3 to perform various tasks. The Safety Supervisor, forexample, may use metabolic state estimates to assess/predict the risk ofout-of-range excursions in blood glucose (i.e. episodes of hypoglycemiaor hyperglycemia).

The biometric values that comprise the “key” metabolic states arespecific to the embodiment of the modular architecture. However, atleast one of the states will be blood glucose concentration. We use{tilde over (x)}(t) to denote the vector of true metabolic state values,reserving the first element of the vector {tilde over (x)}₁(t) to bedefined as blood glucose concentration. It is important to note that thetrue values of the metabolic states are unknown in general because theyare not the same as the input signals g_(c)(t), J(t), EX(t), and M(t).(For example, while plasma glucose and insulin cannot be measured inreal time, values for these states can be estimated from g_(c)(t), J(t),EX(t), and M(t).) We use {tilde over ({circumflex over (x)})}(t) todenote the corresponding vector of state estimates.

Thus, the inputs of the Short Term Observer Module are the same as theoutputs of Data Module shown in FIG. 3 : g_(c)(t), J(t), EX(t), andM(t). The output of the Short Term Observer Module is:

-   -   {tilde over ({circumflex over (x)})}(t)=a vector of estimates of        the key metabolic states of the patient, including plasma        glucose and plasma insulin.

In embodiments of the modular architecture where the Short TerniObserver is not active, the default state estimate is simply apass-through of the glucose sample:

{tilde over ({circumflex over (x)})}^(°)(t)=g _(c)(t)  (2)

Even though the composition of the vectors {tilde over (x)}(t) and (t)is specific to the embodiment, we can describe the basic framework forhow state estimation is performed. We assume that the evolution of thestate vector is described by a discrete-time, nonlinear dynamic model,generally expressed as:

{tilde over (x)}(t+1)={tilde over (F)}({tilde over(x)}(t),J(t),ω_(m)(t),ω_(c)(t)),  (3)

where ω_(m)(t) and ω_(c)(t) are disturbance processes representing mealsand exercise/physical activity. The nonlinear model could be adiscrete-time realization of the oral glucose meal model [16], minimalmodels derived from the meal model, the Hovorka [21] or the Sorensenmodel [55], or another yet to be determined mathematical model ofglucose-insulin-exercise dynamics. The state estimate {tilde over({circumflex over (x)})}(t) is derived through a process of filtering(state “observations”) based on the processed input data g_(c)(t), J(t),EX(t), and M(t). The filtering process could be a direct application ofKalman filtering, extended Kalman filtering, or another yet to bedetermined statistical procedure that incorporates state observation asa key internal process. The state estimation process is driven in partby a model for the relationship between these signals and the underlyingstate vector, expressed as:

CGM(t)={tilde over (G)} _(CGM)({tilde over (x)}(t))+v _(CGM)(t)  (4)

M(t)={tilde over (G)} _(m)(ω_(m)(t))+v _(m)(t)  (5)

EX(t)={tilde over (G)} _(c)(ω_(c)(t))+v _(c)(t),  (6)

where (1) {tilde over (G)}_(CGM), {tilde over (G)}_(m), and {tilde over(G)}_(c) describe the functional relationship between the subject'sactual metabolic state {tilde over (x)}(t) and CGM(t), the actual mealdisturbance ω_(m)(t) and the meal signal y_(m)(t), and the actualexercise/physical activity disturbance ω_(c)(t) and the exercise datasignal y_(c)(t), respectively and (2) v_(CGM)(t), v_(m)(t), and v_(c)(t)are sensor noise processes.

The System Coordinator shown in FIG. 4 plays a central role in themodular architecture, serving (1) to act as a “router” for signals fromand to the various Control Modules and (2) to decompose the estimateblood glucose concentration into parts attributable to (i) meals and theresponse to meals and (ii) all other disturbances (exercise and physicalactivity). The System Coordinator receives a specific set of signalsfrom the various Control Modules, listed in the following paragraphs.

From the Data Module, the System Coordinator receives g_(c)(t), J(t),EX(t), and M(t). From the Long Term Observer Module, the SystemCoordinator receives the behavioral profiles β(τ), η(τ), and γ(τ), and,from the Short Term Observer Module, the System Coordinator receives themetabolic state vector estimate {tilde over (x)}(t). In addition, fromthe Safety Supervision Module, the System Coordinator receives:

-   -   u_(m,actual)(t)=insulin allowed at time t by the Safety        Supervisor as a part of the response to meals    -   u_(r,actual)(t)=insulin allowed at time t by the Safety        Supervisor as a part of the response to non-meal disturbances

From the Meal Control Module (Control Module 3), the CoordinatorReceives

-   -   g_(m)(t)=estimated glucose excursion due to meals (and the        response to meals)

Many of the inputs to the System Coordinator are “passed through” toother control modules, as shown in FIG. 3 .

In addition to acting as a “router” for signals between various modules,the System Coordinator play a central role in attributing glucoseexcursions to either (1) meals and the response to meals or (2) othermetabolic disturbances. Specifically, the System Coordinator serves toevaluate

-   -   g_(r)(t)=an estimate of blood glucose concentration offset by        the estimated contribution of meals (and the response to meals),        computed as

g _(r)(t)={tilde over ({circumflex over (x)})}(t)−g _(m)(t)  (7)

The signal g_(r)(t) is the only output of the System Coordinator that isnot a pass-through signal from other modules. (Note that since outputg_(m)(t) of the Controller defaults to zero if the Meal Control Moduleis not active, then g_(r)(t) is exactly the estimated value of bloodglucose {tilde over (x)}₁(t).)

The Daily Profile Control Module (Control Module 1) of FIG. 4 serves tocompute a reference insulin signal u_(ref)(t), which is analogous to thepatient's daily insulin profile in conventional CSII therapy. The inputsto the Daily Profile Control Module are the outputs of the systemobservers: (1) the metabolic state vector estimate {tilde over({circumflex over (x)})}(t) (produced by the Short Term Observer Module)and (2) the behavioral profiles β(τ), η(τ), and γ(τ) (produced by theLong Term. Observer Module). The output of Daily Profile Control Moduleis:

-   -   u_(ref)(t)=reference insulin infused at time t

The computation of the reference insulin signal is specific to theembodiment of the modular architecture. In an embodiment where the DailyProfile Control Module is inactive, the default output is simply thepatient's daily basal insulin profile.

The Meal Control Module (Control Module 3) of FIG. 4 serves to manage BGexcursions in anticipation of and in response to meals in a quasiopen-loop fashion. It does this by (1) maintaining an open-loop internalestimate of the contribution of meals to the patient's overall BG and(2) using the patient meal behavioral profile to compute recommendedmeal insulin u_(m)(t) both prior to the meal arriving and after themeal.

The inputs to the Meal Controller are

-   -   M(t)=the meal data process (from the Data Module)    -   u_(m,actual)(t)=insulin allowed at time t by the Safety        Supervisor as a part of the response to meals (produced by        Safety Supervision Module)    -   β(τ)=the meal behavioral profile (produced by the Long Term        Observer Module)        The outputs of the Meal Controller (originating within the Meal        Controller) are    -   u_(m)(t)=the recommended insulin infusion at time t for        accommodating meals    -   g_(m)(t)=estimated glucose excursion due to meals (and the        response to meals)

The computation of u_(m)(t) and g_(m)(t) are both specific to theembodiment of the modular architecture. In an embodiment where the mealcontrol module is inactive, the recommended insulin infusion u_(m)(t)defaults to zero, as does g_(m)(t) the estimated glucose excursion dueto meals, leaving the Compensation Control to Target Module (if active)to reject the meal disturbance.

For embodiments in which the Meal Control Module is active, estimationof the glucose excursion due to meals would be computed from an openloop dynamic system model, reflective of the dynamic interactionsbetween glucose and insulin. Whatever dynamic system model is used cangenerally be described as a nonlinear discrete-time system, with statevector ξ(t), whose evolution is dictated by:

ξ(t+1)=F _(m)(ξ(t),u _(m,actual)(t),M(t)).  (8)

where the state space equations defined by the operator F_(m) are suchthat, if u_(m, actual)(t) is fixed at zero and M(t) is also fixed atzero, then 4(t) converges to zero asymptotically. The estimatedexcursion in blood glucose can be derived from the model as

g _(m)(t)=G _(m)(ξ(t)).  (9)

The same dynamic system model could be used in conjunction with the mealbehavioral profile β(τ) to compute an optimal open loop response tomeals, perhaps even in anticipate of meal arrival.

In one embodiment of the modular architecture with an active MealControl Module, the behavioral profile β(τ) could express as theconditional probability p_(k) of the meal arriving at stage k of thecurrent meal regime (given that it has not already arrived). Such a mealbehavioral profile would allow, the administration of insulin inanticipation of meals, without compromising patient safety. In thisembodiment, the suggested meal control signal would be computed as afunction of the control relevant statistics y_(m)(t) and futureconditional probabilities p_(t), p_(t+1), K, p _(k) as

u _(m)(t)=C _(m)(ξ(t),M(t),p _(k) ,p _(t=1) ,K p _(k) ),  (10)

where C_(m) denotes the mathematical transformation of input data tooptimal anticipatory and reactive insulin injections.

The Compensation Control to Target Module of FIG. 4 (Control Module 2)serves to manage in-range excursions of blood glucose that are notalready accounted for by the Meal Control Module (Control Module 3). Inserving in this capacity, this module maintains an internal estimate ofthe patient's “residual” metabolic state based on (1) the meal-offsetglucose signal g_(r)(t) produced by the System Coordinator, pastresidual insulin injections u_(r, actual)(t) allowed by the SafetySupervisor, the exercise data process EX(t), and the exercise behavioraland insulin utilization profiles η(τ) and γ(τ).

The inputs to the Compensation. Controller are

-   -   g_(r)(t)=an estimate of glucose excursions away from g_(ref)(t)        that are not due to meals (computed by the System Coordinator)    -   u_(r,actual)(t)=insulin allowed at time t by the Safety        Supervisor (computed by the Safety Supervisor)    -   EX(t)=the exercise data process from the Data Module)    -   η=exercise behavioral profile (produced by the Long Term        Observer Module)    -   γ=circadian insulin profile (produced by the Long Term Observer        Module)        The output of the Compensation Controller is    -   u_(r)(t)=the recommended “residual” insulin infusion at time t        needed to compensate for non-meal disturbances

The computation of u_(r)(t) is specific to the embodiment of the modulararchitecture. In an embodiment where the Compensation Control to TargetModule is inactive, the default is u_(r)(t)=0. In this case the SafetySupervision Module (in conjunction with the Daily Profile Control.Modeul and Meal Control Module) would take full responsibility forkeeping the patient within an acceptable range of BG values.

In an embodiment of the modular architecture with an active CompensationControl to Target Module, the recommended residual insulin could becomputed using Proportional-Integral-Derivative (ND) control, as in[57], using e(t)=g_(r)(t) g_(ref)(t) as an error signal, whereg_(ref)(t) is a possibly time-varying target.

In other embodiments of the modular architecture (in which theCompensation. Control to Target Module is Active), the recommendedresidual insulin signal could be computed from a dynamic model of theinteractions of g_(r)(t) and residual insulin, which in general termscould be described as:

x(t+1)=F _(r)(x(t),u _(actual)(t),ω_(c)(t))  (11)

where the state space equations defined by the operator F_(r) arc suchthat, if u_(r, actual)(t) is fixed at zero and the exercise disturbanceis also fixed at zero (i.e. no physical activity), then x(t) convergesto a given target value g_(ref) asymptotically. A vector of stateestimates {circumflex over (x)}(t) can be derived (through a process ofstate observation) using measurement models

g _(r)(t)=G _(r)(x(t))  (12)

EX(t)=G _(c)(ω_(c)(t))+v _(c)(t),  (13)

The recommended residual control signal u_(r)(t) is computed based on anestimate {circumflex over (x)}(t) of the state vector x(t) that iscomputed from the residual glucose signal g_(r)(t). In one embodiment ofthe modular architecture, recommended residual control signal would becomputed using closed-loop model predictive control techniques, as in[44] based on the estimate {circumflex over (x)}(t). In otherembodiments, Compensation Control could be achieved via LQG [51], LMPC,or any other closed loop control methodology. In yet other embodiments,compensation control could be achieve via either positive or negativeresidual “boluses”, whose timing and extent are computed based on{circumflex over (x)}(t).

The Safety Supervision Module of FIG. 4 monitors and, if necessary,modifies the suggested insulin injection signals produced by the othercontrol modules, in an effort to (1) avoid hypoglycemia and (2) if theother modules are unable to do so, mitigate sustained hyperglycemia. Inaddition, the safety supervisor is responsible for computing the final“approved” next insulin injection J(t+1) used as the main output of themodular architecture, attributing relevant parts of this signal to an“actual” meal control signal u_(m, actual)(t) and an actual residualcontrol signal u_(r,actual)(t).

The inputs to the Safety Supervisor are

-   -   {tilde over ({circumflex over (x)})}(t)=the vector of estimated        metabolic states for the patient, the first component of which,        {tilde over ({circumflex over (x)})}(t), is an estimate of the        patients blood glucose concentration (Short Term Observer        Module)    -   M(t)=the meal data process (from the Data Module)    -   EX(t)=the exercise data process (form the Data Module)    -   u_(ref)(t) reference insulin infused at time t (produced by the        Daily Profile Control Module (Control Module 1))    -   u_(m)(t)=the recommended insulin infusion at time t for        accommodating meals (produced by the Meal Control Module        (Control Module 3))    -   u_(r)(t)=the recommended residual insulin infusion at time t for        accommodating non-meal disturbances (produced by the        Compensation Control to Target Module (Control Module 2))        The outputs of the Safety Supervisor (all generated by the        Safety Supervisor) are    -   u_(m,actual)(t)=insulin allowed at time t by the Safety        Supervisor as a part of the response to meals    -   u_(r,actual)(t)=insulin allowed at time t by the Safety        Supervisor as a part of the response to non-meal disturbances    -   J(t+1)=total insulin infusion allowed by the Safety Supervisor        at time t        The computation of J(t+1) and the decomposition of J(t+1) into        actual meal and residual components is specific to the        embodiment of the modular architecture. It is a requirement,        however, that all of approved insulin must be account for as        reference, meal, and residual insulin:

J(t+1)=u _(ref)(t)+u _(m,actual)(t)+u _(r,actual)(t)  (14)

In one embodiment of the modular architecture, the computation of J(t+1)would be based on the prediction of future metabolic states {tilde over({circumflex over (x)})}(t+τ), given the current estimate {tilde over({circumflex over (x)})}(t) of the metabolic state vector, the exercisedata process EX(t), and the suggested insulin amounts u_(ref)(t),u_(m)(t), and u_(r)(t) held fixed at their current values:

{tilde over ({circumflex over (x)})}(t+τ|t)=Φ_(r)({tilde over({circumflex over (x)})}(t),EX(t),u _(ref)(t),u _(m)(t),u _(r)(t))  (15)

The predicted future metabolic states {tilde over ({circumflex over(x)})}(t+τ|t) would be factored into an assessment of the risk ofhypoglycemia {circumflex over (R)}(t+r|t) using the risk symmetrizationprocedure of [36,43]. In this embodiment, the total insulin controlsignal would be computed from the suggested values as

$\begin{matrix}{{J\left( {t + 1} \right)} = \frac{{u_{ref}(t)} + {u_{m}(t)} + {u_{r}(t)}}{1 + {k \cdot {\hat{R}\left( {t + \left. \tau \middle| t \right.} \right)}}}} & (16)\end{matrix}$

where k is an aggressiveness factor for the attenuating function of themodule, which would be determined from patient characteristics, such asbody weight, total daily insulin, carb ratio, etc. The differencebetween J(t) and u_(ref)(t)+u_(m)(t)+u_(r)(t) would be attributed tou_(m,actual)(t)+u_(r, actual)(t) according to a proportionalityconstant, α in [0,1], as follows:

u _(m,actual)(t)=α·(J(t+1)−u _(ref)(t))  (17)

u _(r,actual)(t)=(1−α)·(J(t+1)−u _(ref)(t))  (18)

Note that if the risk {circumflex over (R)}(t+τ|t) of hypoglycemia iszero (which would be the case when predicted BG is greater than 112.5mg/dl), then J(t+1)=u_(ref)(t)+u_(m)(t)+u_(r)(t),u_(m,actual)(t)=u_(m)(t), and u_(r,actual)(t)=u_(r)(t).

An analogous computation could be used to compute a total approvedinsulin J(t) to mitigate hyperglycemia, attributing the difference tou_(m,actual)(t) and u_(r,actual)(t).

Turning to FIG. 5 , FIG. 5 is a functional block diagram for a computersystem 500 for implementation of an exemplary embodiment or portion ofan embodiment of present invention. For example, a method or system ofan embodiment of the present invention may be implemented usinghardware, software or a combination thereof and may be implemented inone or more computer systems or other processing systems, such aspersonal digit assistants (PDAs) equipped with adequate memory andprocessing capabilities. In an example embodiment, the invention wasimplemented in software running on a general purpose computer 500 asillustrated in FIG. 5 . The computer system 500 may includes one or moreprocessors, such as processor 504. The Processor 504 is connected to acommunication infrastructure 506 (e.g., a communications bus, cross-overbar, or network). The computer system 500 may include a displayinterface 502 that forwards graphics, text, and/or other data from thecommunication infrastructure 506 (or from a frame buffer not shown) fordisplay on the display unit 539. Display unit 539 may be digital and/oranalog.

The computer system. 500 may also include a main memory 598, preferablyrandom access memory (RAM), and may also include a secondary memory 510.The secondary memory 510 may include, for example, a hard disk drive 512and/or a removable storage drive 514, representing a floppy disk drive,a magnetic tape drive, an optical disk drive, a flash memory, etc. Theremovable storage drive 514 reads from and/or writes to a removablestorage unit 518 in a well known manner. Removable storage unit 518,represents a floppy disk, magnetic tape, optical disk, etc. which isread by and written to by removable storage drive 514. As will beappreciated, the removable storage unit 518 includes a computer usablestorage medium having stored therein computer software and/or data.

In alternative embodiments, secondary memory 510 may include other meansfor allowing computer programs or other instructions to be loaded intocomputer system 500. Such means may include, for example, a removablestorage unit 522 and an interface 520. Examples of such removablestorage units/interfaces include a program cartridge and cartridgeinterface (such as that found in video game devices), a removable memorychip (such as a ROM, PROM, EPROM or EEPROM) and associated socket, andother removable storage units 522 and interfaces 520 which allowsoftware and data to be transferred from the removable storage unit 522to computer system 500.

The computer system 500 may also include a communications interface 524.Communications interface 124 allows software and data to be transferredbetween computer system 500 and external devices. Examples ofcommunications interface 524 may include a modem, a network interface(such as an Ethernet card), a communications port (e.g., serial orparallel, etc.), a PCMCIA slot and card, a modem, etc. Software and datatransferred via communications interface 524 are in the form of signals528 which may be electronic, electromagnetic, optical or other signalscapable of being received by communications interface 524. Signals 528are provided to communications interface 524 via a communications path(i.e., channel) 526. Channel 526 (or any other communication means orchannel disclosed herein) carries signals 528 and may be implementedusing wire or cable, fiber optics, blue tooth, a phone line, a cellularphone link, an RE link, an infrared link, wireless link or connectionand other communications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media or medium such asvarious software, firmware, disks, drives, removable storage drive 514,a hard disk installed in hard disk drive 512, and signals 528. Thesecomputer program products (“computer program medium” and “computerusable medium”) are means for providing software to computer system 500.The computer program product may comprise a computer useable mediumhaving computer program logic thereon. The invention includes suchcomputer program products. The “computer program product” and “computeruseable medium” may be any computer readable medium having computerlogic thereon.

Computer programs (also called computer control logic or computerprogram logic) are may be stored in main memory 508 and/or secondarymemory 510. Computer programs may also be received via communicationsinterface 524. Such computer programs, when executed, enable computersystem 500 to perform the features of the present invention as discussedherein. In particular, the computer programs, when executed, enableprocessor 504 to perform the functions of the present invention.Accordingly, such computer programs represent controllers of computersystem 500.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system 500 using removable storage drive 514, hard drive 512 orcommunications interface 524. The control logic (software or computerprogram logic), when executed by the processor 504, causes the processor504 to perform the functions of the invention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above may be implemented in SPSS control language or C++programming language, but could be implemented in other variousprograms, computer simulation and computer-aided design, computersimulation environment, MATLAB, or any other software platform orprogram, windows interface or operating system. (or other operatingsystem) or other programs known or available to those skilled in theart.

An embodiment of the present invention provides for, but not limitedthereto, the development of an open-loop advisory system assistingpatients with diabetes in the control of their blood glucose level, or aclosed-loop control system (known as artificial pancreas) controllingautomatically blood glucose. Accordingly, an aspect may provide, but notlimited thereto, a complex project involving the development and theimplementation of multiple mathematical algorithms, methods andengineering solutions.

Some aspects of an embodiment of the present invention diabetes controlsystem, method and computer program product provides, but not limitedthereto, the following: open-loop or closed-loop control must adapt toindividual physiologic characteristics and to fast changingenvironmental factors; and keys to this adaptation are biosystem(patient) observation and modular control. Consequently, an aspect of anembodiment of the present invention diabetes control system, method andcomputer program product establishes the foundation for a modular systemcomprised of algorithmic observers of patients' behavior and metabolicstate, and control modules responsible for insulin delivery andhypoglycemia prevention.

A component of such a modular system is the System Coordinator, whichintegrates the action of observers, control, and safety modules in orderto:

-   -   In open-loop advisory mode, advise the patient about changes in        basal rate, need for insulin boluses, or need to discontinue        insulin delivery and take a preventive action due to increased        risk for hypoglycemia.    -   In closed-loop control mode, command directly the insulin pump        and issue warning for impending hypoglycemia to the patient if        insulin pump shut-off would be insufficient to eliminate that        risk.

Moreover, an embodiment of the present invention includes a modularapproach enabled by a System Coordinator that will permit incrementaltesting and deployment of system features—observers and controlmodules—which will structure and facilitate system development.

Existing open- and closed-loop control algorithms do not include modulararchitecture—typically a single control module is implemented andcharged with the function of delivering insulin regardless of the causesof glucose fluctuation. A modular approach enabled by a SystemCoordinator of an embodiment of the present invention allows thedistribution of control and safety functions among specialized controlmodules that are then integrated by the System Coordinator.

A modular architecture of open-loop advisory system or closed-loopcontrol system and method has many advantages, including but not limitedto: the possibility of incremental development, testing, and deploymentof control modules, and the possibility of using existing modules forincorporation in the system. Centralizing the system integration andcoordination functions into a System Coordinator alleviates the separatecontrol modules from the need of working in regimes that are unsuitablefor their design. For example, a deterministic MPC is poorly suited toaccount for the stochastic nature of carbohydrate intake during meals,while a stochastic impulse control is poorly suited for maintaining asteady basal rate. Employing a System Coordinator permits differenttypes of specialized algorithms to function together, each within therealm of its optimal performance.

The Modular Architecture and the System Coordinator included in anembodiment of the invention is suitable for implementation in open-loopadvisory systems or closed-loop control systems for diabetes. Thesesystems would typically use a continuous glucose monitor and an insulinpump, linked by the System Coordinator to optimize glucose control indiabetes. Other sources of information (e.g. heart rate monitoring thatenables the recognition of exercise) can be included in the system aswell, as long as interface with the System Coordinator is established.

It should be appreciated that as discussed herein, a subject may be ahuman or applicable in principle to animals. It should be appreciatedthat an animal may be a variety of any applicable type, including, butnot limited thereto, mammal, veterinarian animal, livestock animal orpet type animal, etc. As an example, the animal may be a laboratoryanimal specifically selected to have certain characteristics similar tohuman (e.g. rat, dog, pig, monkey), etc. It should be appreciated thatthe subject may be any applicable human patient, for example.

EXAMPLES

Practice of an aspect of an embodiment (or embodiments) of the inventionwill be still more fully understood from the following examples, whichare presented herein for illustration only and should not be construedas limiting the invention in any way.

An aspect of an embodiment of the present invention provides astructure, system, or method for a diabetes control system. Anembodiment of the structure or related method may comprise: modules forprocessing and storing data; conduits (or the like) between modules; andsignals produced in the event that certain modules are not insertedwithin the structure. The structure may comprise differentimplementations of modules that can be inserted and/or interchanged. Themodules for processing and storing data may be configured to include oneor more of the following: one or more data acquisition modules; one ormore observer modules; one or more routing modules; one or more controlmodules; and one or more safety modules. The structure may furthercomprise an insulin injector for injecting insulin based on the outputof the one or more safety modules. One or more of the data acquisitionmodules may receive one or more of the following types of information:continuous glucose monitoring data; insulin pump data; exercise data;and meal data. The exercise data may include heart rate information,motion sensor information, and other indicators of physical activity.The meal data may include acknowledgements of meals as they arrive.

The one or more data acquisition modules may be configured to output oneor more of the following types of information: a single, processedglucose sample at a specific time, or a history of glucose samples up toa specific time, or a statistic computed from glucose samples up to aspecific time; a most recent actual insulin pump command, or a historyof recent insulin pump commands up to a specific time, or a statisticcomputed from recent commands; exercise process data at the specifictime; and meal data at the specific time. The one or more observermodules may be configured to receive one or more of the following typesof information: a single, processed glucose sample at a specific time,or a history of glucose samples up to a specific time, or a statisticcomputed from glucose samples up to a specific time; a most recentactual insulin pump command, or a history of recent insulin pumpcommands up to a specific time, or a statistic computed from recentcommands; exercise process data at the specific time; and meal data atthe specific time. The one or more observer modules may be configured toprocess one or more of the following: metabolic measurements; metabolicdisturbances; and metabolic treatments.

The metabolic measurements may include one or more of the following:continuous glucose measurements; and insulin measurements. The metabolicdisturbances may include one or more of the following: meals; andexercise. The metabolic treatments may include one or more of thefollowing: insulin injections; other pharmaceuticals (hormones)associated with the management of diabetes; treatments for hypoglycemia;and glucagon injections. The other pharmaceuticals may include hormones.The treatments for hypoglycemia may include administering rescuecarbohydrates and/or glucagon injections.

The one or more observer modules may be configured to construct andupdate an internal representation or estimate of a metabolic state of anindividual. The one or more observer modules may be configured totransmit metabolic state information to the one or more control modules.The one or more observer modules may be configured to keep an internalrepresentation of a behavioral pattern of an individual. The one or moreobserver modules may be configured to assess risks of undesirableevents. The one or more observer modules may include a short termobserver module. The short term observer module may contain informationrelating to: metabolic state; meat excursion; and/or metabolic state andmeal excursion. The short term observer module observes X times perhour, where X is 0<X<7200. It should be appreciated that the frequencymay be greater or less as desired or required. The short term observermodule may be configured to output one of the following: a vector ofestimates of key metabolic states of an individual; a single, processedglucose sample at a specific time, or a history of glucose samples up toa specific time, or a statistic computed from glucose samples up to aspecific time; or both the vector of estimates of key metabolic statesand the sample or history of processed glucose samples. The vector ofestimates may include plasma glucose and plasma insulin.

The one or more observer modules may include a long term observermodule. The long term observer module may contain information relatingto behavioral profiles. The behavioral profiles may be daily behavioralprofiles, but may vary as desired or required. The long term observermodule may assess behavioral profiles X times per month, where X is0<X<60. It should be appreciated that the frequency may be greater orless as desired or required. The long term observer module may beconfigured to output one or more of the following types of information:a daily profile of an individual's meal behavior as a function of thetime of day; a daily profile of the individual's exercise behavior as afunction of the tune of day; and a daily profile of the individual'sutilization of insulin as a function of the time of day. The dailyprofile of the individual's meal behavior as a function of the time ofday may include probabilities of eating at various times throughout theday and/or probabilities of taking various meals defined bycarbohydrate, protein, and fat content. The daily profile of theindividual's exercise behavior as a function of the time of day mayinclude probabilities of various levels of physical activity throughoutthe day. The daily profile of the individual's utilization of insulin asa function of the time of day may include a trend analysis for meal andcorrection boluses and/or a trend analysis for basal rate profiles. Thedaily profile of an individual's meal behavior may define aprobabilistic description of the individual's eating behavior withingiven meal regimes throughout a day. The behavioral data may be used todefine a patient's breakfast regime and then assessing the probabilityof taking breakfast at any specific point of time within that regime.

The one or more routing modules may include a system coordinator. Thesystem coordinator may be configured to coordinate the distribution ofinput signals to control one or more modules and allocate differentsegments of diabetes management to different control modules. The systemcoordinator may be configured to receive one or more of the followingtypes of information: a single, processed glucose sample at a specifictime, or a history of glucose samples up to a specific time, or astatistic computed from glucose samples up to a specific time; a mostrecent actual insulin pump command, or a history of recent insulin pumpcommands up to a specific time, or a statistic computed from recentcommands; exercise process data at the specific time; meal data at thespecific time; a vector of estimates of key metabolic states of anindividual; a daily profile of an individual's meal behavior as afunction of the time of day; a daily profile of the individual'sexercise behavior as a function of the time of day; a daily profile ofthe individual's utilization of insulin as a function of the time ofday; insulin allowed at the specific time by the one or more safetymodules as part of a response to meals; insulin allowed at the specifictime by the one or more safety modules as part of a response to non-mealdisturbances; and estimated glucose excursion due to meals. The systemcoordinator may be configured to output one or more of the followingtypes of information: a most recent actual insulin pump command;exercise process data at the specific time; meal data at the specifictime; a vector of estimates of key metabolic states of an individual; adaily profile of an individual's meal behavior as a function of the timeof day; a daily profile of the individual's exercise behavior as afunction of the time of day; a daily profile of the individual'sutilization of insulin as a function of the time of day; insulin allowedat the specific time by the one or more safety modules as part of aresponse to meals; insulin allowed at the specific time by the one ormore safety modules as part of a response to non-meal disturbances;estimated glucose excursion due to meals; and an estimate of bloodglucose concentration offset by the estimated contribution of meals.

The one or more control modules may be configured to perform: dailyprofile control; compensation control to target; and/or meal control.The daily profile control may include determining a basal rate baselinesetting throughout the day, or some other period as desired or required.The compensation control to target may include small adjustments to thebasal rate baseline to correct hyperglycemia and reduce the likelihoodof hypoglycemia. The meal control may include a schedule of meal insulinfollowing acknowledgement of a particular meal.

The one or more control modules may be configured to include: a firstcontrol module to calculate and suggest a basal insulin delivery; asecond control module to calculate and suggest compensation of the basaldelivery in case of non-meal-related deviations; and a third controlmodule to calculate and suggest meal insulin boluses. The first controlmodule may be configured to receive one or more of the following typesof information: a vector of estimates of key metabolic states of anindividual; a daily profile of an individual's meal behavior as afunction of the time of day; a daily profile of the individual'sexercise behavior as a function of the time of day; and a daily profileof the individual's utilization of insulin as a function of the time ofday. The first control module may be configured to output a referenceinsulin infused at a specific time. The second control module may beconfigured to receive one or more of the following types of information:an estimate of blood glucose concentration offset by the estimatedcontribution of meals; insulin allowed at the specific time by the oneor more safety modules as part of a response to non-meal disturbances;exercise process data at the specific time; a daily profile of theindividual's exercise behavior as a function of the time of day; and adaily profile of the individual's utilization of insulin as a functionof the time of day. The second control module may be configured tooutput a recommended residual insulin infusion at a specific time tocompensate for non-meal-related disturbances. The third control modulemay be configured to receive one or more of the following types ofinformation: meal data at the specific time; insulin allowed at thespecific time by the one or more safety modules as part of a response tomeals; and a daily profile of an individual's meal behavior as afunction of the time of day. The third control module may be configuredto output one or more of the following types of information: arecommended insulin infusion at a specific time for accommodating meals;and estimated glucose excursion due to meals. The second control modulemay compensate for a basal delivery up or down. The third control modulemay be configured to further calculate and suggests pre-meal primingboluses. Moreover, the first control module may operate approximatelydaily. It should be appreciated that the frequency may be greater orless as desired or required. The second control modules may operateapproximately every fifteen to thirty minutes. It should be appreciatedthat the frequency may be greater or less as desired or required. Thethird control module may operate approximately every several hours. Itshould be appreciated that the frequency may be greater or less asdesired or required.

The one or more safety modules may be configured to include a systemsupervision module. The system supervision module may be configured toreceive one or more of the following types of information: exerciseprocess data at the specific time; meal data at the specific time; avector of estimates of key metabolic states of an individual; referenceinsulin infused at a specific time; a recommended insulin infusion at aspecific time for accommodating meals; and a recommended residualinsulin infusion at a specific time to compensate for non-meal-relateddisturbances. The system supervision module may be configured to outputone or more of the following types of information: insulin allowed at aspecific time by the one or more safety modules as part of a response tomeals; insulin allowed at the specific time by the one or more safetymodules as part of a response to non-meal disturbances; and totalinsulin infusion allowed by the safety supervision module at thespecific time. The safety supervision module may be configured to:receive information from the one or more observer modules and one ormore control modules; determine whether there is an increased risk ofhypoglycemia or hyperglycemia; if it is determined that there is anincreased risk of hypoglycemia, automatically reduce or discontinue asuggested infusion; and if it is determined that there is an increasedrisk of hyperglycemia, automatically notify the user of the risk. Theincreased risk of hypoglycemia may be defined as an increased risk ofupcoming hypoglycemia or an increased risk of prolonged hypoglycemia.The suggested infusion may be an insulin infusion or a glucagoninfusion. The safety supervision module may further comprise a displaydevice for displaying real-time information to an individual. Theindividual may be a subject or a doctor, or other user as desired orrequired. The real-time information may include one or more of thefollowing: a warning of hypoglycemia; a warning of hyperglycemia; asuggestion to reduce insulin delivery; and a suggestion to reject orreduce pre-meal or correction boluses.

Unless defined otherwise, all technical terms used herein have the samemeanings as commonly understood by one of ordinary skill in the art oftreating diabetes. Specific methods, devices, and materials aredescribed in this application, but any methods and materials similar orequivalent to those described herein can be used in the practice of thepresent invention. While embodiments of the invention have beendescribed in some detail and by way of exemplary illustrations, suchillustration is for purposes of clarity of understanding only, and isnot intended to be limiting. Various terms have been used in thedescription to convey an understanding of the invention; it will beunderstood that the meaning of these various terms extends to commonlinguistic or grammatical variations or forms thereof. It will also beunderstood that when terminology referring to devices, equipment, ordrugs has used trade names, brand names, or common names, that thesenames are provided as contemporary examples, and the invention is notlimited by such literal scope. Terminology that is introduced at a laterdate that may be reasonably understood as a derivative of a contemporaryterm or designating of a subset of objects embraced by a contemporaryterm will be understood as having been described by the now contemporaryterminology. Further, while some theoretical considerations have beenadvanced in furtherance of providing an understanding, for example, ofthe quantitative interrelationships among carbohydrate consumption,glucose levels, and insulin levels, the claims to the invention are notbound by such theory. Moreover, any one or more features of anyembodiment of the invention can be combined with any one or more otherfeatures of any other embodiment of the invention, without departingfrom the scope of the invention. Still further, it should be understoodthat the invention is not limited to the embodiments that have been setforth for purposes of exemplification, but is to be defined only by afair reading of claims that are appended to the patent application,including the full range of equivalency to which each element thereof isentitled.

Unless clearly specified to the contrary, there is no requirement forany particular described or illustrated activity or element, anyparticular sequence or such activities, any particular size, speed,material, duration, contour, dimension or frequency, or any particularlyinterrelationship of such elements. Moreover, any activity can berepeated, any activity can be performed by multiple entities, and/or anyelement can be duplicated. Further, any activity or element can beexcluded, the sequence of activities can vary, and/or theinterrelationship of elements can vary. It should be appreciated thataspects of the present invention may have a variety of sizes, contours,shapes, compositions and materials as desired or required.

In summary, while the present invention has been described with respectto specific embodiments, many modifications, variations, alterations,substitutions, and equivalents will be apparent to those skilled in theart. The present invention is not to be limited in scope by the specificembodiment described herein. Indeed, various modifications of thepresent invention, in addition to those described herein, will beapparent to those of skill in the art from the foregoing description andaccompanying drawings. Accordingly, the invention is to be considered aslimited only by the spirit and scope of the following claims, includingall modifications and equivalents.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthis application. For example, regardless of the content of any portion(e.g., title, field, background, summary, abstract, drawing figure,etc.) of this application, unless clearly specified to the contrary,there is no requirement for the inclusion in any claim herein or of anyapplication claiming priority hereto of any particular described orillustrated activity or element, any particular sequence of suchactivities, or any particular interrelationship of such elements.Moreover, any activity can be repeated, any activity can be performed bymultiple entities, and/or any element can be duplicated. Further, anyactivity or element can be excluded, the sequence of activities canvary, and/or the interrelationship of elements can vary. Unless clearlyspecified to the contrary, there is no requirement for any particulardescribed or illustrated activity or element, any particular sequence orsuch activities, any particular size, speed, material, dimension orfrequency, or any particularly interrelationship of such elements.Accordingly, the descriptions and drawings are to be regarded asillustrative in nature, and not as restrictive. Moreover, when anynumber or range is described herein, unless clearly stated otherwise,that number or range is approximate. When any range is described herein,unless clearly stated otherwise, that range includes all values thereinand all sub ranges therein. Any information in any material (e.g., aUnited States/foreign patent, United States/foreign patent application,book, article, etc.) that has been incorporated by reference herein, isonly incorporated by reference to the extent that no conflict existsbetween such information and the other statements and drawings set forthherein. In the event of such conflict, including a conflict that wouldrender invalid any claim herein or seeking priority hereto, then anysuch conflicting information in such incorporated by reference materialis specifically not incorporated by reference herein.

RECITED PUBLICATIONS

The following patents, applications and publications as listed below andthroughout this document are hereby incorporated by reference in theirentirety herein.

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REFERENCES

The devices, structures, systems, computer program products, and methodsof various embodiments of the invention disclosed herein may utilizeaspects disclosed in the following references, applications,publications and patents and which are hereby incorporated by referenceherein in their entirety:

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1-9. (canceled)
 10. A non-transitory computer readable medium, having stored thereon processor-executable instructions that when executed cause a processor to: receive metabolic measurement data; generate metabolic state estimate data of a patient based on one or more of insulin pump data, exercise data, or meal data; generate a total insulin infusion signal based on a computed output, the computed output including: an insulin infusion signal in response to the metabolic state estimate data; a recommended meal insulin infusion signal in response to meal intake data; and/or a recommended residual insulin infusion signal in response to glucose level excursion data, the glucose level excursion data being caused by non-meal occurrences; wherein the computed output is based on one or more of the metabolic measurement data or the metabolic state estimate data of the patient; and transmit the total insulin infusion signal to an insulin delivery device.
 11. The non-transitory computer readable medium of claim 10, wherein: the instructions cause the processor to generate the computed output.
 12. The non-transitory computer readable medium of claim 10, wherein: the instructions cause the processor to: transmit one or more of the metabolic measurement data or the metabolic state estimate data of the patient to a controller device, wherein the controller device generates the computed output; and receive the computed output from the controller device to generate the total insulin infusion signal.
 13. The non-transitory computer readable medium of claim 10, wherein: the computed output includes: an insulin infusion signal in response to the metabolic state estimate data; a recommended meal insulin infusion signal in response to meal intake data; and a recommended residual insulin infusion signal in response to glucose level excursion data, the glucose level excursion data being caused by non-meal occurrences.
 14. The non-transitory computer readable medium of claim 10, wherein: the metabolic state estimate data of the patient is based on insulin pump data, exercise data, and meal data.
 15. The non-transitory computer readable medium of claim 10, wherein: the insulin delivery device is an insulin pump.
 16. The non-transitory computer readable medium of claim 10, wherein: the instructions cause the processor to transmit the total insulin infusion signal to the insulin delivery device via a closed loop control.
 17. The non-transitory computer readable medium of claim 10, wherein: the total insulin infusion signal is a control signal configured to cause the insulin delivery device to administer insulin.
 18. The non-transitory computer readable medium of claim 10, wherein: the instructions cause the processor to receive the one or more of insulin pump data, exercise data, or meal data from an external data source.
 19. The non-transitory computer readable medium of claim 10, wherein: the instructions cause the processor to receive the one or more of insulin pump data, exercise data, or meal data from a data acquisition controller.
 20. The non-transitory computer readable medium of claim 10, wherein: the computed output is further based on a vector of state estimate and/or a behavior profile.
 21. The non-transitory computer readable medium of claim 20, wherein: the vector of state estimate is based on data assessed at X times per hour, where 0<X≤7200; and the behavioral profile is based on data assessed at X times per month, where 0<X≤60.
 22. An insulin delivery device, comprising: a processor configured to receive a signal from a computing environment, the computing environment including a non-transitory computer readable medium having instructions for causing the computing environment to: receive metabolic measurement data; generate metabolic state estimate data of a patient based on one or more of insulin pump data, exercise data, or meal data; generate a total insulin infusion signal based on a computed output, the computed output including: an insulin infusion signal in response to the metabolic state estimate data; a recommended meal insulin infusion signal in response to meal intake data; and/or a recommended residual insulin infusion signal in response to glucose level excursion data, the glucose level excursion data being caused by non-meal occurrences; wherein the computed output is based on one or more of the metabolic measurement data or the metabolic state estimate data of the patient; and transmit the total insulin infusion signal to the processor of the insulin delivery device; wherein the processor of the insulin delivery device administers insulin based on the total insulin infusion signal.
 23. The insulin delivery device of claim 22, wherein: the computed output includes: an insulin infusion signal in response to the metabolic state estimate data; a recommended meal insulin infusion signal in response to meal intake data; and a recommended residual insulin infusion signal in response to glucose level excursion data, the glucose level excursion data being caused by non-meal occurrences.
 24. The insulin delivery device of claim 22, wherein: the metabolic state estimate data of the patient is based on insulin pump data, exercise data, and meal data.
 25. The insulin delivery device of claim 22, wherein: the insulin delivery device is an insulin pump.
 26. The insulin delivery device of claim 22, wherein: the processor of the insulin delivery device is configured to receive the total insulin infusion signal via a closed loop control.
 27. The insulin delivery device of claim 22, wherein: the computed output is further based on a vector of state estimate and/or a behavior profile.
 28. The insulin delivery device of claim 27, wherein: the vector of state estimate is based on data assessed at X times per hour, where 0<X≤7200; and the behavioral profile is based on data assessed at X times per month, where 0<X≤60.
 29. The insulin delivery device of claim 22, wherein: the processor of the insulin delivery device administers insulin by injecting an amount of insulin based on the total insulin infusion signal and/or adjusting an amount of insulin to be injected based on the total insulin infusion signal. 